A Computer Vision-Based Quality Assessment Technique for R2R Printed Silver Conductors on Flexible Plastic Substrates
Abstract
:1. Introduction
2. Materials and Methods
3. Results
4. Discussion and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Søndergaard, R.R.; Hösel, M.; Krebs, F.C. Roll-to-Roll Fabrication of Large Area Functional Organic Materials. J. Polym. Sci. Part B Polym. Phys. 2013, 51, 16–34. [Google Scholar] [CrossRef]
- Van De Wiel, H.J.; Galagan, Y.; Van Lammeren, T.J.; De Riet, J.F.J.; Gilot, J.; Nagelkerke, M.G.M.; Lelieveld, R.H.C.A.T.; Shanmugam, S.; Pagudala, A.; Hui, D.; et al. Roll-to-Roll Embedded Conductive Structures Integrated into Organic Photovoltaic Devices. Nanotechnology 2013, 24, 484014. [Google Scholar] [CrossRef] [PubMed]
- Hösel, M.; Angmo, D.; Søndergaard, R.R.; dos Reis Benatto, G.A.; Carlé, J.E.; Jørgensen, M.; Krebs, F.C. High-Volume Processed, ITO-Free Superstrates and Substrates for Roll-to-Roll Development of Organic Electronics. Adv. Sci. 2014, 1, 1400002. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Abbel, R.; Galagan, Y.; Groen, P. Roll-to-Roll Fabrication of Solution Processed Electronics. Adv. Eng. Mater. 2018, 20, 1701190. [Google Scholar] [CrossRef] [Green Version]
- Schwartz, E.L.; Schwartz, E.; Ober, C. Roll to Roll Processing for Flexible Electronics; Cornell University: Ithaca, NY, USA, 2006. [Google Scholar]
- Sumaiya, S.; Kardel, K.; El-Shahat, A. Organic Solar Cell by Inkjet Printing—An Overview. Technologies 2017, 5, 53. [Google Scholar] [CrossRef] [Green Version]
- Palavesam, N.; Marin, S.; Hemmetzberger, D.; Landesberger, C.; Bock, K.; Kutter, C. Roll-to-Roll Processing of Film Substrates for Hybrid Integrated Flexible Electronics. Flex. Print. Electron. 2018, 3, 014002. [Google Scholar] [CrossRef]
- Søndergaard, R.; Hösel, M.; Angmo, D.; Larsen-Olsen, T.T.; Krebs, F.C. Roll-to-Roll Fabrication of Polymer Solar Cells. Mater. Today 2012, 15, 36–49. [Google Scholar] [CrossRef] [Green Version]
- Amini, A.; Kanfoud, J.; Gan, T.H. An Artificial-Intelligence-Driven Predictive Model for Surface Defect Detections in Medical MEMS. Sensors 2021, 21, 6141. [Google Scholar] [CrossRef] [PubMed]
- Qian, X.; Zhang, H.; Zhang, H.; Wu, Y.; Diao, Z.; Wu, Q.E.; Yang, C. Solar Cell Surface Defects Detection Based on Computer Vision. Int. J. Perform. Eng. 2017, 13, 1048–1056. [Google Scholar] [CrossRef]
- Zikulnig, J.; Mühleisen, W.; Bolt, P.; Simor, M.; De Biasio, M. Photoluminescence Imaging for the In-Line Quality Control of Thin-Film Solar Cells. Solar 2022, 2, 1–11. [Google Scholar] [CrossRef]
- Zheng, H.; Zhou, L.; Marks, R.; Happonen, T.; Kraft, T.M. Defect Recognition of Roll-to-Roll Printed Conductors Using Dark Lock-In Thermography and Localized Segmentation. Appl. Sci. 2022, 12, 2005. [Google Scholar] [CrossRef]
- Agarwal, A.; Jawahar, C.V.; Narayanan, P.J. A Survey of Planar Homography Estimation Techniques; Tech. Rep. IIIT/TR/2005/12; Center for Visual Information Technology: Hyderabad, India, 2005; pp. 1–25. [Google Scholar]
- Di Stefano, L.; Bulgarelli, A. A Simple and Efficient Connected Components Labeling Algorithm. In Proceedings of the International Conference on Image Analysis and Processing, ICIAP 1999, Venice, Italy, 27–29 September 1999; pp. 322–327. [Google Scholar] [CrossRef]
- Amini, A.; Banitsas, K.; Cosmas, J. A Comparison between Heuristic and Machine Learning Techniques in Fall Detection Using Kinect V2. In Proceedings of the 2016 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Benevento, Italy, 15–18 May 2016; pp. 1–6. [Google Scholar] [CrossRef]
image name: Sample 075 timestamp: 2022-05-02 20:27:26.365079 | 20 sub-image name: CH_WO_08L264 quality %: 90.44 health condition: Defective |
0 sub-image name: AH_WO_08L232_9 quality %: 90.45 health condition: Defective | 21 sub-image name: CH_WO_1L264 quality %: 96.28 health condition: Healthy |
1 sub-image name: AH_WO_1L232_9 quality %: 99.0 health condition: Defective | 22 sub-image name: CH_WO_3L264 quality %: 98.26 health condition: Healthy |
2 sub-image name: AH_WO_3L232_9 quality %: 99.19 health condition: Defective | 23 sub-image name: CH_WO_5L264 quality %: 99.61 health condition: Healthy |
3 sub-image name: AH_WO_5L232_9 quality % 98.19 health condition: Healthy | 24 sub-image name: CH_W1L264 quality %: 96.47 health condition: Healthy |
4 sub-image name: AH_W1L232_9 quality %: 98.62 health condition: Healthy | 25 sub-image name: CV_WO_08L264 quality %: 98.13 health condition: Defective |
5 sub-image name: AV_WO_08L232_9 quality %: 99.85 health condition: Defective | 26 sub-image name: CV_WO_1L264 quality %: 98.89 health condition: Healthy |
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Amini, A.; Gan, T.-H. A Computer Vision-Based Quality Assessment Technique for R2R Printed Silver Conductors on Flexible Plastic Substrates. Appl. Sci. 2023, 13, 1084. https://doi.org/10.3390/app13021084
Amini A, Gan T-H. A Computer Vision-Based Quality Assessment Technique for R2R Printed Silver Conductors on Flexible Plastic Substrates. Applied Sciences. 2023; 13(2):1084. https://doi.org/10.3390/app13021084
Chicago/Turabian StyleAmini, Amin, and Tat-Hean Gan. 2023. "A Computer Vision-Based Quality Assessment Technique for R2R Printed Silver Conductors on Flexible Plastic Substrates" Applied Sciences 13, no. 2: 1084. https://doi.org/10.3390/app13021084
APA StyleAmini, A., & Gan, T.-H. (2023). A Computer Vision-Based Quality Assessment Technique for R2R Printed Silver Conductors on Flexible Plastic Substrates. Applied Sciences, 13(2), 1084. https://doi.org/10.3390/app13021084